class_docstr: The Latent Context Embedding Additive (LCE-A) Kernel.
  
  This kernel is similar to the SACKernel, and is used when context breakdowns are
  unbserverable. It assumes the same additive structure and a spatial kernel shared
  across contexts. Rather than assuming independence, LCEAKernel models the
  correlation in the latent functions for each context through learning context
  embeddings.
docstr: Iterate across each partition of parameter space and sum the
  covariance matrices together
docstr_tok: ["Iterate","across","each","partition","of","parameter","space","and","sum","the","covariance","matrices","together"]
code: def forward(
      self,
      x1: Tensor,
      x2: Tensor,
      diag: bool = False,
      last_dim_is_batch: bool = False,
      **params: Any,
  ) -> Tensor:
      """Iterate across each partition of parameter space and sum the
      covariance matrices together
      """
      # context covar matrix
      context_covar = (
          self._eval_context_covar()
          if self.training
          else self._context_covar
      )
      base_covar_perm = self._eval_base_covar_perm(x1, x2)
      # expand context_covar to match base_covar_perm
      if base_covar_perm.dim() > context_covar.dim():
          context_covar = context_covar.expand(base_covar_perm.shape)
      # then weight by the context kernel
      # compute the base kernel on the d parameters
      einsum_str = (
          "...nnki, ...nnki -> ...n" if diag else "...ki, ...ki -> ..."
      )
      covar_dense = torch.einsum(
          einsum_str, context_covar, base_covar_perm
      )
      if diag:
          return DiagLinearOperator(covar_dense)
      return DenseLinearOperator(covar_dense)
  
imports: from typing import Any, Dict, List, Optional
  import torch
  from gpytorch.constraints import Positive
  from gpytorch.kernels.kernel import Kernel
  from gpytorch.kernels.matern_kernel import MaternKernel
  from gpytorch.priors.torch_priors import GammaPrior
  from linear_operator.operators import DiagLinearOperator
  from linear_operator.operators.dense_linear_operator import DenseLinearOperator
  from torch import Tensor
  from torch.nn import ModuleList
label_desc: Use image node_id 12 for calling the LCEAKernel obj's underlying member method code with example usage: obj.forward(x1, x2, diag, last_dim_is_batch) and returns: DenseLinearOperator, DiagLinearOperator